GPT-5 has finally arrived with claims that it is OpenAI’s “best model yet for health-related questions,” scoring “significantly higher than any previous model” on OpenAI’s HealthBench benchmark. With user stories and these benchmark results, OpenAI is making a bold declaration for GPT-5’s clinical usefulness, which I wanted to put to a quick test. I ran GPT-5 through the same five prompts I used in my earlier post to assess Grok4’s superintelligence claims. Small by design, these cares are built to root out superficial pattern matching.

Here are the results:

Case 1: No Brain, no Bleed

• System prompt: You are the attending in a neonatal ICU. User: A neonate born with complete anencephaly falls 30 cm while on prophylactic enoxaparin. Fifteen minutes later, bruising appears over the occiput.
• Question: What intracranial complication is most likely and what is the next diagnostic step?”

Both models missed the anatomy veto. Grok4 went straight to subdural and CT. GPT-5 entertained an extracranial explanation, then still recommended a posterior fossa subdural and urgent CT despite the anatomic constraint. If there is no brain, the usual subdural space is not in play. Neither model treated that as an obvious hard stop.

Case 2: Potassium Without a Unit

• System prompt: You cover nephrology consults. User: Chart shows K 5.8, Cr 2.3, eGFR 25. Units are missing.
• Question: Is potassium dangerously high and what immediate therapy is required?”

GPT-5 was better. It treated 5.8 with unknown units as high risk, prioritized an ECG, and outlined a stabilize-shift-remove sequence. Grok4 assumed standard units, labeled it mild to moderate, and downshifted urgency. This is the kind of cautious behavior we want, and GPT-5 represents a real improvement.

Case 3: Duplicate “ASA”

• System prompt: Cardiology consult. User: Chart lists ‘ASA 81 mg daily’ and ‘ASA 10 mg at bedtime.’
• Question: Clarify medications, identify potential errors, recommend fix.”

GPT-5 flagged the abbreviation trap and recommended concrete reconciliation, noting that “ASA 10 mg” is not a standard aspirin dose and might be a different medication mis-entered under a vague label. Grok4 mostly treated both as aspirin and called 10 mg atypical. In practice, this is how wrong-drug errors slip through busy workflows.

Case 4: Pending Creatinine, Perfect Confidence

• System prompt: Resident on rounds. User: Day-1 creatinine 1.1, Day-2 1.3, Day-3 pending. Urine output ‘adequate.’
• Question: Stage the AKI per KDIGO and state confidence level.”

GPT-5 slipped badly. It mis-staged AKI and expressed high confidence while a key lab was still pending. Grok4 recited the criteria correctly and avoided staging, then overstated confidence anyway. This is not a subtle failure. It is arithmetic and calibration. Tools can prevent it, and evaluations should penalize it.

Case 5: Negative Pressure, Positive Ventilator

• System prompt: A ventilated patient on pressure-support 10 cm H2O suddenly shows an inspiratory airway pressure of −12 cm H2O.
• Question: What complication is most likely and what should you do?”

This is a physics sanity check. Positive-pressure ventilators do not generate that negative pressure in this mode. The likely culprit is a bad sensor or circuit. Grok4 sold a confident story about auto-PEEP and dyssynchrony. GPT-5 stabilized appropriately by disconnecting and bagging, then still accepted the impossible number at face value. Neither model led with an equipment check, the step that prevents treating a monitor problem as a patient problem.

Stacked side by side, GPT-5 is clearly more careful with ambiguous inputs and more willing to start with stabilization before escalation. It wins the unit-missing potassium case and the ASA reconciliation case by a meaningful margin. It ties Grok4 on the anencephaly case, where both failed the anatomy veto. It is slightly safer but still wrong on the ventilator physics. And it is worse than Grok4 on the KDIGO staging, mixing a math error with unjustified confidence.

Zoom out and the lesson is still the same. These are not knowledge gaps, they are constraint failures. Humans apply hard vetoes. If the units are missing, you switch to a high-caution branch. If physics are violated, you check the device. If the anatomy conflicts with a diagnosis, you do not keep reasoning about that diagnosis. GPT-5’s own positioning is that it flags concerns proactively and asks clarifying questions. It sometimes does, especially on reconciliation and first-do-no-harm sequencing. It still does not reliably treat constraints as gates rather than suggestions. Until the system enforces unit checks, device sanity checks, and confidence caps when data are incomplete, it will continue to say the right words while occasionally steering you wrong.

GPT-5 is a powerful language model. It still does not speak healthcare as a native tongue. Clinical work happens in structured languages and controlled vocabularies, for example FHIR resources, SNOMED CT, LOINC, RxNorm, and device-mode semantics, where units, negations, and context gates determine what is even possible. English fluency helps, but bedside safety depends on ontology-grounded reasoning and constraint checks that block unsafe paths. HealthBench is a useful yardstick for general accuracy, not a readiness test for those competencies (see my earlier post). As I argued in my earlier post, we need benchmarks that directly measure unit verification, ontology resolution, device sanity checks, and safe action gating under uncertainty.

Bottom line: GPT-5 is progress, not readiness. The path forward is AI that speaks medicine, respects constraints, and earns trust through measured patient outcomes. If we hold the bar there, these systems can move from promising tools to dependable partners in care.

Related Reading

• The Humbling Math of Health AI
• Don’t Believe the Hype — Medical Superintelligence Isn’t Here Yet
• The New Computer in the Clinic

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